Processing Results from DADA2 to make plots, do some statistics

Link to notebook

Link to github repo.

Load packages

library(tidyverse)
library(readxl)
library(phyloseq)
#library(dada2)
library(Biostrings)
#library(DECIPHER)
library(phangorn)
library(readr)
library(seqinr)
library(decontam)
library(ape)
library(vegan)
#library(philr)
library(RColorBrewer)
library(microbiome)
#library(DESeq2)
library(compositions);
library(cowplot)
library(plotly)
library(htmlwidgets)
library(withr)
library(lubridate)

Import and prepare the data from eDNA

Import Metadata

metadata <- read_csv("sample_data.csv")

── Column specification ────────────────────────────────────────────────────────────────────────────────────
cols(
  SampleID = col_character(),
  `Year.Trawl#` = col_character(),
  Datecode = col_double(),
  Date = col_character(),
  Month = col_double(),
  Year = col_double(),
  Bayside = col_character(),
  Station = col_character()
)

Import DADA2 results:

Import count table and taxonomy file. I slightly modified otutable.csv in Excel to otutable_mod.csv to remove the quotes around seq names and put NA placehoder as first col name (which was above row names)

# Import Count table. Skip first row of tsv file, which is just some text
count_table <- read_table2("results/otutable_mod.csv")

Missing column names filled in: 'X1' [1]
── Column specification ────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  X1 = col_character()
)
ℹ Use `spec()` for the full column specifications.

colnames(count_table)[1] <- "SampleID"

# Import taxonomy of ASVs
taxonomy <- read_csv(file="results/tax_sequences_blast_taxonomy.csv")

Missing column names filled in: 'X1' [1]Duplicated column names deduplicated: 'RefSeq_Tax_ID' => 'RefSeq_Tax_ID_1' [18]
── Column specification ────────────────────────────────────────────────────────────────────────────────────
cols(
  X1 = col_double(),
  ASV_ID = col_character(),
  ref_seq_ID = col_character(),
  PID = col_double(),
  alnmt_len = col_double(),
  mismatch = col_double(),
  eval = col_double(),
  bscore = col_double(),
  RefSeq_Tax_ID = col_double(),
  Ref_Seq_title = col_character(),
  superkingdom = col_character(),
  phylum = col_character(),
  class = col_character(),
  order = col_character(),
  family = col_character(),
  genus = col_character(),
  species = col_character(),
  RefSeq_Tax_ID_1 = col_double()
)

# remove first col of sequential numbers
taxonomy[,1] <- NULL
# filter out sequences with low PID (recommended by Sara)
taxonomy <- filter(taxonomy, PID > 92)

# remove BLAST metadata and just retain taxonomy (necessary for further processing below)
drop.cols <- c(colnames(taxonomy)[2:9],'RefSeq_Tax_ID_1')
taxonomy <-  select(taxonomy, -one_of(drop.cols))


# And import the Common names, as curated by Sara. Join to taxonomy
commonnames <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",7)
commonnames

taxonomy <- left_join(taxonomy, commonnames, by = "ASV_ID")
taxonomy

NA

Filtering removed seqs 110, 332 (Gobiosoma ginsburgi and Belone belone) Note for Sara should we consider setting this at 97% which is more robust and still leaves 334 unique ASVs (rather than 379 with the 92% cutoff in the settings above)

Preview datasets

count_table

taxonomy

metadata

Make phyloseq object

I want to use the phyloseq package for some plotting/ statistics, which first requires making phyloseq objects out of each of input data tables-

count_table_matrix <- as.matrix(count_table[,2:392]) # convert count table to matrix, leaving out character column of sample ID
rownames(count_table_matrix) <- count_table$SampleID # add back in Sample IDs as row names
ASV =   otu_table(count_table_matrix, taxa_are_rows =  FALSE)

taxonomy_matrix <- as.matrix(taxonomy[,2:9])
rownames(taxonomy_matrix) <- taxonomy$ASV_ID 
TAX =   tax_table(taxonomy_matrix)

META    =   sample_data(data.frame(metadata, row.names = metadata$`SampleID`))

First check that the inputs are in compatible formats by checking for ASV names with the phyloseq function, taxa_names

head(taxa_names(TAX))

[1] "Seq_1" "Seq_2" "Seq_3" "Seq_4" "Seq_5" "Seq_6"

head(taxa_names(ASV))

[1] "Seq_1" "Seq_2" "Seq_3" "Seq_4" "Seq_5" "Seq_6"

And check sample names were also detected

# Modify taxa names in ASV, which are formatted with the sample ID, underscor, fastq ID. Don't need this fastq ID anymore and want it to match the sample names from metadata
sample_names(ASV) <-  sample_names(ASV) %>%
  str_replace_all(pattern = "_S[:digit:]+",replacement = "")


head(sample_names(ASV))

[1] "T1PosCon" "T1S10"    "T1S11"    "T1S1"     "T1S2"     "T1S3"    

head(sample_names(META))

[1] "T1PosCon" "T1S1"     "T1S2"     "T1S3"     "T1S5"     "T1S6"    

And make the phyloseq object

ps <- phyloseq(ASV, TAX,    META)

Quality check and filtering- eDNA

Rarefaction curves

rarecurve(otu_table(ps), step=50, cex=0.5)

empty rows removed

# save as .eps
setEPS()
postscript("Figures/rarefaction.eps")

rarecurve(otu_table(ps), step=50, cex=0.5)

empty rows removed

dev.off()

quartz_off_screen 
                2 

Most samples look like they were sampled to completion. Be weary of T3S11, T1S2, and maybe T4S5

Filtering

Check some features of the phyloseq object

rank_names(ps)

[1] "superkingdom" "phylum"       "class"        "order"        "family"       "genus"        "species"     
[8] "CommonName"  

unique(tax_table(ps)[, "superkingdom"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        superkingdom
Seq_1   "Eukaryota" 
Seq_377 NA          

unique(tax_table(ps)[, "phylum"])

Taxonomy Table:     [3 taxa by 1 taxonomic ranks]:
        phylum      
Seq_1   "Chordata"  
Seq_368 "Arthropoda"
Seq_377 NA          

unique(tax_table(ps)[, "class"])

Taxonomy Table:     [5 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_63  "Mammalia"      
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       
Seq_377 NA              

There are some ASVs with NA as superkingdom, phylum, or class annotation- delete these.

ps <- subset_taxa(ps, !is.na(superkingdom) & !is.na(phylum) & !is.na(class))

unique(tax_table(ps)[, "superkingdom"])

Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
      superkingdom
Seq_1 "Eukaryota" 

unique(tax_table(ps)[, "phylum"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        phylum      
Seq_1   "Chordata"  
Seq_368 "Arthropoda"

unique(tax_table(ps)[, "class"])

Taxonomy Table:     [4 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_63  "Mammalia"      
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       

nrow(tax_table(ps)) # number of ASVs left

[1] 378

378 ASVs still remain…

Also check class Mammalia, to see if contamination or real:

tax_table(subset_taxa(ps, class == 'Mammalia'))

Taxonomy Table:     [8 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class      order          family      genus   species        CommonName 
Seq_63  "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_88  "Eukaryota"  "Chordata" "Mammalia" "Artiodactyla" "Suidae"    "Sus"   "Sus scrofa"   "Wild boar"
Seq_157 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_343 "Eukaryota"  "Chordata" "Mammalia" "Carnivora"    "Felidae"   "Felis" "Felis catus"  "Cat"      
Seq_369 "Eukaryota"  "Chordata" "Mammalia" "Artiodactyla" "Bovidae"   "Bos"   "Bos taurus"   "Cattle"   
Seq_378 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_383 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_389 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    

These are human, wild boar, cat (…cat lady), and cattle. All are contamination so delete all Mammalia

ps <- subset_taxa(ps, !class == 'Mammalia')
unique(tax_table(ps)[, "class"])

Taxonomy Table:     [3 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       

Next check the “Insecta” entries

tax_table(subset_taxa(ps, class == 'Insecta'))

Taxonomy Table:     [2 taxa by 8 taxonomic ranks]:
        superkingdom phylum       class     order         family       genus         species             
Seq_368 "Eukaryota"  "Arthropoda" "Insecta" "Hymenoptera" "Formicidae" "Linepithema" "Linepithema humile"
Seq_380 "Eukaryota"  "Arthropoda" "Insecta" "Hymenoptera" "Formicidae" "Linepithema" "Linepithema humile"
        CommonName
Seq_368 "Ant"     
Seq_380 "Ant"     

The onlly Insecta is Linepithema humile, which are ants so delete these too..

ps <- subset_taxa(ps, !class == 'Insecta')
unique(tax_table(ps)[, "class"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_362 "Chondrichthyes"

Plot total sequences by phyla to check out sequencing effort

Check overall how the phyla are distributed among samples

# First aglomerate the ASVs at the phylum level using the phyloseq function, tax_glom
superkingdomGlommed = tax_glom(ps, "superkingdom")

# and plot
plot_bar(superkingdomGlommed, x = "Sample")

ggsave(filename = "Figures/seqdepth.eps", plot = plot_bar(superkingdomGlommed, x = "Sample"), units = c("in"), width = 9, height = 6, dpi = 300, )# and save

Total sequences reveals certain samples had very low sequencing effort: T1S7, T1S8, T3S11, and, not as bad, T1S2 and T4S5

The rarefaction analysis also showed T1S2 and T4S5 samples were likely not sequenced to completion. Therefore remove these 5 samples from analysis

ps <- subset_samples(ps, !SampleID == "T1S7" & !SampleID == "T1S8" & !SampleID == "T3S11" & !SampleID == "T1S2" & !SampleID == "T4S5")

ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 50 samples ]
sample_data() Sample Data:       [ 50 samples by 8 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

50 samples remaining with 368 ASVs

Remove Pos Controls (all hits in positive controls are the same family- I assume this is expected)

ps <- subset_samples(ps, !SampleID == "T1PosCon" & !SampleID == "T2PosCon" & !SampleID == "T3PosCon")
ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 8 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

And lastly, correct some taxonomy: According to Sara, Engraulis encrasicolus (European anchovy) should be Anchoa mitchilli (Bay anchovy):

tax_table(ps) <- gsub(tax_table(ps), pattern = "Engraulis encrasicolus", replacement = "Anchoa mitchilli")  

47 samples remainwith 368 unique ASVs

Abundance plots- eDNA

For plotting, use relative abundances (# of ASV sequences/sum total sequences in sample), calculated easily using microbiome::transform

ps_ra <- microbiome::transform(ps, transform = "compositional")

Export the relative abundance matrix so Sara can have it:

# Extract abundance matrix from the phyloseq object
RelAbun_matrix = as(otu_table(ps_ra), "matrix")

# Coerce to data.frame
RelAbun_dataframe = as.data.frame(RelAbun_matrix)

# Export
write.csv(RelAbun_dataframe,"results/otutable_relabun.csv", row.names = TRUE)

Plot abundance of families from all samples to do some quality control

Then aglomerate the ASVs at the family level using the phyloseq function, tax_glom

familyGlommed_RA = tax_glom(ps_ra, "family")
family_barplot <- plot_bar(familyGlommed_RA, x = "Sample", fill = "family")
family_barplot

NOTES for Sara

• There are some samples, (T1S3, T1S6, T2S11, T3S10, T3S4, T3S5, T3S9, T4S4, T4S7, T5S7) which are composed almost exclusively of 1 family. This might be fine, but I’m not used to seeing this with prokaroytic data. Just want to check with you

Agglomerate by species to see if I get the same 38 unique species Sara sees:

speciesGlommed_RA = tax_glom(ps_ra, "CommonName")
speciesGlommed_RA

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 43 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 8 sample variables ]
tax_table()   Taxonomy Table:    [ 43 taxa by 8 taxonomic ranks ]

tax_table(speciesGlommed_RA)

Taxonomy Table:     [43 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class            order                family           
Seq_1   "Eukaryota"  "Chordata" "Actinopteri"    "Atheriniformes"     "Atherinopsidae" 
Seq_2   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Clupeidae"      
Seq_3   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Engraulidae"    
Seq_4   "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Pomatomidae"    
Seq_5   "Eukaryota"  "Chordata" "Actinopteri"    "Lutjaniformes"      "Lutjanidae"     
Seq_6   "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Paralichthyidae"
Seq_7   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Clupeidae"      
Seq_9   "Eukaryota"  "Chordata" "Actinopteri"    "Gobiiformes"        "Gobiidae"       
Seq_10  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Scophthalmidae" 
Seq_11  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Serranidae"     
Seq_12  "Eukaryota"  "Chordata" "Actinopteri"    "Spariformes"        "Sparidae"       
Seq_15  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"     
Seq_16  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"     
Seq_17  "Eukaryota"  "Chordata" "Actinopteri"    "Labriformes"        "Labridae"       
Seq_19  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Cottidae"       
Seq_20  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Pleuronectidae" 
Seq_21  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Moronidae"      
Seq_22  "Eukaryota"  "Chordata" "Actinopteri"    "Syngnathiformes"    "Syngnathidae"   
Seq_30  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Paralichthyidae"
Seq_33  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"     
Seq_34  "Eukaryota"  "Chordata" "Actinopteri"    "Labriformes"        "Labridae"       
Seq_36  "Eukaryota"  "Chordata" "Actinopteri"    "Anguilliformes"     "Anguillidae"    
Seq_38  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"     
Seq_40  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Gasterosteidae" 
Seq_44  "Eukaryota"  "Chordata" "Actinopteri"    "Cyprinodontiformes" "Fundulidae"     
Seq_50  "Eukaryota"  "Chordata" "Actinopteri"    "Atheriniformes"     "Atherinopsidae" 
Seq_52  "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Phycidae"       
Seq_54  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"     
Seq_57  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Triglidae"      
Seq_67  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"     
Seq_82  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"     
Seq_84  "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Gadidae"        
Seq_102 "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Engraulidae"    
Seq_103 "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Cottidae"       
Seq_115 "Eukaryota"  "Chordata" "Actinopteri"    "Cyprinodontiformes" "Fundulidae"     
Seq_119 "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Phycidae"       
Seq_139 "Eukaryota"  "Chordata" "Actinopteri"    "Batrachoidiformes"  "Batrachoididae" 
Seq_141 "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"     
Seq_181 "Eukaryota"  "Chordata" "Actinopteri"    "Tetraodontiformes"  "Tetraodontidae" 
Seq_231 "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Merlucciidae"   
Seq_359 "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Triglidae"      
Seq_362 "Eukaryota"  "Chordata" "Chondrichthyes" "Myliobatiformes"    "Myliobatidae"   
Seq_372 "Eukaryota"  "Chordata" "Chondrichthyes" "Carcharhiniformes"  "Triakidae"      
        genus                species                         CommonName                
Seq_1   "Menidia"            "Menidia menidia"               "Atlantic silverside"     
Seq_2   "Brevoortia"         "Brevoortia tyrannus"           "Atlantic menhaden"       
Seq_3   "Engraulis"          "Anchoa mitchilli"              "Bay anchovy"             
Seq_4   "Pomatomus"          "Pomatomus saltatrix"           "Bluefish"                
Seq_5   "Lutjanus"           "Lutjanus griseus"              "Grey snapper"            
Seq_6   "Paralichthys"       "Paralichthys dentatus"         "Summer flounder"         
Seq_7   "Alosa"              "Alosa mediocris"               "Hickory shad"            
Seq_9   "Gobiosoma"          "Gobiosoma ginsburgi"           "Seaboard goby"           
Seq_10  "Scophthalmus"       "Scophthalmus aquosus"          "Windowpane flounder"     
Seq_11  "Centropristis"      "Centropristis striata"         "Black seabass"           
Seq_12  "Stenotomus"         "Stenotomus chrysops"           "Scup"                    
Seq_15  "Leiostomus"         "Leiostomus xanthurus"          "Spot"                    
Seq_16  "Menticirrhus"       "Menticirrhus saxatilis"        "Northern kingfish"       
Seq_17  "Tautoga"            "Tautoga onitis"                "Tautog"                  
Seq_19  "Myoxocephalus"      "Myoxocephalus aenaeus"         "Grubby sculpin"          
Seq_20  "Pseudopleuronectes" "Pseudopleuronectes americanus" "Winter flounder"         
Seq_21  "Morone"             "Morone saxatilis"              "Striped bass"            
Seq_22  "Syngnathus"         "Syngnathus fuscus"             "Northern pipefish"       
Seq_30  "Etropus"            "Etropus microstomus"           "Smallmouth flounder"     
Seq_33  "Cynoscion"          "Cynoscion regalis"             "Weakfish"                
Seq_34  "Tautogolabrus"      "Tautogolabrus adspersus"       "Cunner"                  
Seq_36  "Anguilla"           "Anguilla rostrata"             "American eel"            
Seq_38  "Thunnus"            "Thunnus obesus"                "Bigeye tuna"             
Seq_40  "Apeltes"            "Apeltes quadracus"             "Stickleback"             
Seq_44  "Fundulus"           "Fundulus majalis"              "Striped killifish"       
Seq_50  "Membras"            "Membras martinica"             "Rough silverside"        
Seq_52  "Urophycis"          "Urophycis floridana"           "Spotted hake"            
Seq_54  "Scomber"            "Scomber japonicus"             "Chub mackerel"           
Seq_57  "Prionotus"          "Prionotus carolinus"           "Northern searobin"       
Seq_67  "Thunnus"            "Thunnus thynnus"               "Atlantic bluefin tuna"   
Seq_82  "Bairdiella"         "Bairdiella chrysoura"          "American silver perch"   
Seq_84  "Microgadus"         "Microgadus tomcod"             "Atlantic tomcod"         
Seq_102 "Engraulis"          "Engraulis mordax"              "Bay anchovy"             
Seq_103 "Myoxocephalus"      "Myoxocephalus quadricornis"    "Fourhorn sculpin"        
Seq_115 "Fundulus"           "Fundulus heteroclitus"         "Mummichog"               
Seq_119 "Urophycis"          "Urophycis floridana"           "Red hake"                
Seq_139 "Opsanus"            "Opsanus tau"                   "Oyster toadfish"         
Seq_141 "Katsuwonus"         "Katsuwonus pelamis"            "Skipjack tuna"           
Seq_181 "Sphoeroides"        "Sphoeroides maculatus"         "Northern puffer"         
Seq_231 "Merluccius"         "Merluccius bilinearis"         "Silver hake"             
Seq_359 "Prionotus"          "Prionotus evolans"             "Striped searobin"        
Seq_362 "Rhinoptera"         "Rhinoptera bonasus"            "Cownose ray"             
Seq_372 "Mustelus"           "Mustelus canis"                "Dusky smooth-hound shark"

NOTES for Sara

• I am getting 43 unique species- which ones am I missing that should be removed?
• Also there are two species you are calling Bay anchovy- Engraulis mordax and Anchoa mitchilli. Should the Engraulis mordax be changed to Anchoa mitchilli, similar to Engraulis encrasicolus ?

Bubble plots

Based on my previous scripts with Cariaco Eukaryotic data

# convert ps object to dataframe using phyloseq's psmelt
species_df <- psmelt(speciesGlommed_RA)

# replace zeroes in the table with NA
species_df[species_df == 0] <- NA

# and remove rows with NAs in abundance  (this is so they don't appear as small dots in plot)
species_df <-  filter(species_df, !is.na(Abundance))

Plot by species, scientific name

speciesbubbleplot_eDNA_sciname <- ggplot(species_df, aes(x = Station, y = fct_rev(species), color = Station)) + # the fancy stuff around y (species) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing
scale.

speciesbubbleplot_eDNA_sciname

Plot by species common name

speciesbubbleplot_eDNA_comname <- ggplot(species_df, aes(x = Station, y = fct_rev(CommonName), color = Station)) + # the fancy stuff around y (CommonName) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing
scale.

speciesbubbleplot_eDNA_comname

Exportfigures

ggsave(filename = "Figures/speciesbubbleplot_eDNA_sciname.eps", plot = speciesbubbleplot_eDNA_sciname, units = c("in"), width = 7, height = 12, dpi = 300)

ggsave(filename = "Figures/speciesbubbleplot_eDNA_comname.eps", plot = speciesbubbleplot_eDNA_comname, units = c("in"), width = 7, height = 12, dpi = 300)

NOTE on above. The common name plot has two entries in the Bay anchovy row because, as mentioned above, there are two different species name that are labelled as Bay Anchovy. Is it OK to group these as same species (Anchoa mitchilli)

Import and prepare the data from trawls

Import Trawl Master sheet

# import 4th sheet from  Excel file which contains morphometric data for each individual collected for every date
trawl_master <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",4)
trawl_master

# and import 6th sheet which is station info
stations <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",6)
stations

NA

Convert to count table

Make an equivalent to an OTU table, grouping by date and location and representing counts for every unique species

trawl_counts <- trawl_master %>%
  group_by(DATECODE, STATION_NO, CommonName) %>%
  tally(name = "count")

trawl_counts

and link station names instead of numbers to count table

trawl_counts <- left_join(trawl_counts, stations, by = "STATION_NO")
trawl_counts

Remove 09/16/20 since there is no equivalent eDNA from that date

trawl_counts <- trawl_counts %>%
  filter(DATECODE != "20200916")

trawl_counts

Abundance plots- Trawls

speciesbubbleplot_trawl_comname <- ggplot(trawl_counts, aes(x = STATION_NA, y = fct_rev(CommonName), color = STATION_NA)) + 
  geom_point(aes(size = log10(count), fill = STATION_NA), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(.01,.1, .3, .5, 1, 3), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Log(counts)", fill = "Station")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(DATECODE~BAYSIDE, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing
scale.

speciesbubbleplot_trawl_comname

Export figure

ggsave(filename = "Figures/speciesbubbleplot_trawl_comname.eps", plot = speciesbubbleplot_trawl_comname, units = c("in"), width = 6.75, height = 13, dpi = 300)

Compare Trawl and eDNA

Count unique species across all stations, grouped by date, for each method (trawl, eDNA)

trawl_uniques <- trawl_counts %>%
  group_by(DATECODE, CommonName) %>%
  summarise(Trawl_Count = sum(count, na.rm=TRUE))

`summarise()` regrouping output by 'DATECODE' (override with `.groups` argument)

trawl_uniques

eDNA_uniques <- species_df%>%
  group_by(Datecode, CommonName) %>%
  summarise(eDNA_RelAbun = sum(Abundance, na.rm=TRUE))

`summarise()` regrouping output by 'Datecode' (override with `.groups` argument)

eDNA_uniques

# Combine into one dataframe
trawl_eDNA_abun_table <- full_join(trawl_uniques, eDNA_uniques, by=c("CommonName" = "CommonName", "DATECODE" = "Datecode"))

trawl_eDNA_abun_table

Count total number of species from each method for each date

eDNA_richness <- tally(eDNA_uniques, name = "eDNA")
trawl_richness <- tally(trawl_uniques, name = "trawl")

speciesrichness <- full_join(eDNA_richness, trawl_richness, c("Datecode" = "DATECODE"))
speciesrichness <- pivot_longer(speciesrichness, !Datecode, names_to = "Method", values_to = "Richness")

speciesrichness$Datecode <- ymd(speciesrichness$Datecode) # convert to date format (better for plotting)

speciesrichness

Plot side-by-side

species_richness_plot <- ggplot(speciesrichness, aes(x =Datecode, y = Richness)) +
  geom_line(aes(color = Method), size = 3) +
  theme_bw() +
  xlab("") +
  ylab("Species Richness")

species_richness_plot

# export plot
ggsave(filename = "Figures/species_richness_plot.eps", plot = species_richness_plot, units = c("in"), width = 4, height = 3, dpi = 300)

Sum total number of species across all dates/ stations for entire study

species_sums_abun_table <- trawl_eDNA_abun_table %>%
  group_by(CommonName) %>%
  summarise(Trawl = sum(Trawl_Count, na.rm=TRUE), eDNA = (sum(eDNA_RelAbun, na.rm=TRUE))) %>%
  pivot_longer(!CommonName, names_to = "Method", values_to = "Abundance")

`summarise()` ungrouping output (override with `.groups` argument)

  
# turn zeroes to NA so they don't plot 
species_sums_abun_table <- na_if(species_sums_abun_table,0)

species_sums_abun_table

For each species, plot side-by-side comparison of abundance (summed over whole study) using each method

# First create a custom color scale to make this pretty
myColors <- colorRampPalette(brewer.pal(11,"Spectral"))(55)
names(myColors) <- levels(unique(species_sums_abun_table$CommonName))
colScale <- scale_colour_manual(name = "CommonName",values = myColors)

species_abun_sum_plot <- ggplot(species_sums_abun_table, aes(x = Abundance, y = reorder(CommonName, Abundance, function(x){sum(x,na.rm = TRUE)}), color = CommonName)) +
  geom_point(size = 5) +
  facet_wrap(~fct_rev(Method), scales = "free") +
  theme_bw() +
  xlab("Abundance") +
  ylab("") + 
  colScale +
  theme(legend.position = "none")

species_abun_sum_plot

Export plot

ggsave(filename = "Figures/species_abun_sum_plot.eps", plot = species_abun_sum_plot, units = c("in"), width = 7, height = 8, dpi = 300)

Ordinations

PCA

PCA is essentially a type of PCoA using the Euclidean distance matrix as input. When combined with a log-ratio transformation of the count table, this is deemed appropriate for compositional datasets. It is also recommended as a first step in exploratory analyses of sequencinging datasets.

First do a CLR, centered log ratio transformation of the absolute abundance data (after filtering), as suggested by Gloor et al. 2017

# Estimate covariance matrix for CLR-transformed ASV table
clr_asv_table_ps <- data.frame(compositions::clr(otu_table(ps)))

Generate the PCA and visualize axes

# Generate a Principle Component Analysis (PCA) and evaluated based on the eigen decomposition from sample covariance matrix. 
lograt_pca <- prcomp(clr_asv_table_ps) 
# NOTE- this is equivalent to first making a Euclidean distance matrix using the CLR data table and then running a PCoA. A Euclidean distance matrix of a log-transformed data table = an Aitchison distance matrix. So this is equivalent to the compositional methods listed in Gloor et al.

# Visual representation with a screeplot
lograt_variances <- as.data.frame(lograt_pca$sdev^2/sum(lograt_pca$sdev^2)) %>% #Extract axes
  # Format to plot
  select(PercVar = 'lograt_pca$sdev^2/sum(lograt_pca$sdev^2)') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(lograt_variances)

# Plot screeplot
ggplot(lograt_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Log-Ratio PCA Screeplot, CLR Tranformation")

Plot in 3D using first 3 axes since the 2nd and 3rd are similar proportions of variance. Total variance explained by first three: 15.7 + 10.5 + 10.0 = 36.2%)

Visualize the PCA-

# Extract variances from the clr pca

There were 14 warnings (use warnings() to see them)

pca_lograt_frame <- data.frame(lograt_pca$x) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pca_lograt_frame <- left_join(pca_lograt_frame, metadata, by = "SampleID")
head(pca_lograt_frame)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(lograt_variances[,2], digits = 4)*100

# Plotly - 3-D
pca_lograt <- plot_ly(pca_lograt_frame, type='scatter3d', mode='markers',
        x=~PC1,y=~PC2,z=~PC3,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='CLR-Euclidean PCA',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pca_lograt

# save in "Embedded_figures" dirctory so that it can be hosted at Github and linked in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pca_lograt), file="pca_lograt.html"))

# plot so that it renders in github
htmltools::tags$iframe(
  src=file.path(getwd(), "Embedded_figures/pca_lograt.html"),
  width="100%",
  height="600",
  scrolling="no",
  seamless="seamless",
  frameBorder="0"
)

NA

The CLR-Euclidean PCA reveals there is some separation according to East vs West. The PCA only explains ~36% of the variance so keep going with different ordinations to see if we can get a better representation

PCoA

The more traditional approach to ordinations is to do a PCoA on a distance matrix such as Bray-Curtis, Jaccard, or Unifrac. While these are not considered compositional approaches, when combined with pre-treatment (transformations) they become more appropriate. One such transformation that I will use here is the Hellinger transformation.

The different distance matrices also tell you a few different things about the dataset so I will run through this to try to see if I can tease those out.

Before calculating any distance matrix, do a transformation of the filtered count table. Hellinger transformation is the square root of the relative abundance, so calculate it based on the ps_ra object:

ps_hellinger <- transform_sample_counts(ps_ra, function(x){sqrt(x)})

First, Jaccard, which builds the distance matrix based on presence/absence between samples. It does not take into account relative abundance of the taxa. Therefore this functions well for determining differences driven by rare taxa, which are weighed the same as abundant taxa.

jac_dmat<-vegdist(otu_table(ps_hellinger),method="jaccard") # Jaccard dist metric
pcoa_jac<-ape::pcoa(jac_dmat) # perform PCoA

# Extract variances from pcoa, from jaccard calculated dist. metric
jac_variances <- data.frame(pcoa_jac$values$Relative_eig) %>% 
  select(PercVar = 'pcoa_jac.values.Relative_eig') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(jac_variances)

# Make a screeplot
ggplot(jac_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Jaccard PCoA Screeplot")

The first two axes (19.0 + 9.6 = 28.6) are OK. But I am going to experiment and plot the first 3 axes since the 2nd and 3rd explain a similar amount of variance, 19.6 and 8.4% respectively

Plot in 3D with Plotly

# Extract variances from the jaccard pcoa
pcoa_jac_df <- data.frame(pcoa_jac$vectors) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pcoa_jac_df <- left_join(pcoa_jac_df, metadata, by = "SampleID")
head(pcoa_jac_df)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(jac_variances[,2], digits = 4)*100

# Plotly - 3-D
pcoa_jaccard <- plot_ly(pcoa_jac_df, type='scatter3d', mode='markers',
        x=~Axis.2,y=~Axis.3,z=~Axis.1,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='PCoA Jaccard Distance',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
pcoa_jaccard

`arrange_()` is deprecated as of dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

withr::with_dir('Figures', htmlwidgets::saveWidget(as_widget(pcoa_jaccard), file="pcoa_jaccard.html"))

The Jaccard-PCoA shows separation along axis 2 in East vs West differences.

Next, try a Bray-Curtis distance matrix with PCoA, which builds the distance matrix based on presence/absence between samples and relative abundance differences. This ordination will represent well the differences in samples that are driven by taxa with high relative abundances.

bray_dmat<-vegdist(otu_table(ps_hellinger),method="bray") # Bray-Curtis dist metric
pcoa_bray<-ape::pcoa(bray_dmat) # perform PCoA

# Extract variances from pcoa, from jaccard calculated dist. metric
bray_variances <- data.frame(pcoa_bray$values$Relative_eig) %>% 
  select(PercVar = 'pcoa_bray.values.Relative_eig') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(bray_variances)

# Make a screeplot
ggplot(bray_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Bray-Curtis PCoA Screeplot")

The first two axes (27.7 + 14.3 = 42%) are pretty good again but I am still going to experiment in the plot with the 3rd axis since it is similar to the second (12.2% variance)

Plot in 3D with Plotly

# Extract variances from the jaccard pcoa
pcoa_bray_df <- data.frame(pcoa_bray$vectors) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pcoa_bray_df <- left_join(pcoa_bray_df, metadata, by = "SampleID")
head(pcoa_bray_df)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(bray_variances[,2], digits = 4)*100

# Plotly - 3-D
pcoa_bray <- plot_ly(pcoa_bray_df, type='scatter3d', mode='markers',
        x=~Axis.2,y=~Axis.3,z=~Axis.1,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%  
  layout(font=list(size=12),
         title='PCoA Bray-Curtis Distance',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
pcoa_bray

withr::with_dir('Figures', htmlwidgets::saveWidget(as_widget(pcoa_bray), file="pcoa_bray.html"))

These results are similar to Jaccard: the second axis seems driven by differences in East vs West. But there are clearly other things going on here with axies 1 and 3. I think this is a good representation of the data: together the 3 axes explain 54.13% of the variance.

NMDS

Lastly, try a non-metric dimensional scaling ordination. PCA/PCoA are metric and attempt to rotate axes to fit the distance matrix distribution. An NMDS represents the data in 2-axes, by constraining the distribution of the points. Similar to above, this can be combined with different pre-treatment of the data.

First try the compositional approach, an NMDS on CLR-tranformed data using the Euclidean distances (aka Aitchison distance)

euc_dmat<-dist(clr_asv_table_ps, method = "euclidean") # Build the Aitchison distance matrix
euc_nmds <- metaMDS(euc_dmat, k=2, autotransform=FALSE) # Run the ordination

Run 0 stress 0.2105436 
Run 1 stress 0.2110814 
Run 2 stress 0.2130335 
Run 3 stress 0.2337817 
Run 4 stress 0.2311286 
Run 5 stress 0.2110906 
Run 6 stress 0.2224957 
Run 7 stress 0.2158067 
Run 8 stress 0.2110925 
Run 9 stress 0.2233104 
Run 10 stress 0.2316347 
Run 11 stress 0.2107091 
... Procrustes: rmse 0.005974315  max resid 0.03264245 
Run 12 stress 0.2314 
Run 13 stress 0.2287184 
Run 14 stress 0.2127398 
Run 15 stress 0.2272337 
Run 16 stress 0.2160322 
Run 17 stress 0.2320417 
Run 18 stress 0.2145287 
Run 19 stress 0.2156851 
Run 20 stress 0.2110731 
*** No convergence -- monoMDS stopping criteria:
     1: no. of iterations >= maxit
    19: stress ratio > sratmax

euc_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.05 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.2105436

# Extract points from nmds and merge into data frame with metadata 
euc_nmds_df <- data.frame(euc_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
euc_nmds_df <- left_join(euc_nmds_df, metadata, by = "SampleID")
head(euc_nmds_df)

## Plotting euclidean distance NMDS
nmds_aitch <- ggplot(euc_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Aitchison Distance NMDS, Stress = ', round(euc_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_aitch

ggsave("figures/nmds_aitch.eps",nmds_aitch, width = 7, height = 5, units = c("in"))

The above has a relatively high stress (>0.2) so should be interpreted with caution. But it does show some separation East vs West along NMDS 1.

Next try a Jaccard NMDS, which will represent differences in presence/absence among samples, emphasizing both abundant and rare taxa the same

jac_nmds <- metaMDS(jac_dmat, k=2, autotransform=FALSE) # Run the ordination. Distance matrix was already calculated above

Run 0 stress 0.1627003 
Run 1 stress 0.1633416 
Run 2 stress 0.1662452 
Run 3 stress 0.1625701 
... New best solution
... Procrustes: rmse 0.004322226  max resid 0.02355899 
Run 4 stress 0.1496845 
... New best solution
... Procrustes: rmse 0.09101825  max resid 0.3182899 
Run 5 stress 0.1511997 
Run 6 stress 0.1573356 
Run 7 stress 0.1573408 
Run 8 stress 0.1744426 
Run 9 stress 0.1574038 
Run 10 stress 0.1574293 
Run 11 stress 0.1625698 
Run 12 stress 0.1713826 
Run 13 stress 0.1498312 
... Procrustes: rmse 0.01157208  max resid 0.06868134 
Run 14 stress 0.150871 
Run 15 stress 0.1707482 
Run 16 stress 0.1705187 
Run 17 stress 0.1508708 
Run 18 stress 0.1721511 
Run 19 stress 0.1508709 
Run 20 stress 0.1496849 
... Procrustes: rmse 0.000227522  max resid 0.001291969 
... Similar to previous best
*** Solution reached

jac_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.5 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.1496845

# Extract points from nmds and merge into data frame with metadata 
jac_nmds_df <- data.frame(jac_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
jac_nmds_df <- left_join(jac_nmds_df, metadata, by = "SampleID")
head(jac_nmds_df)

## Plotting euclidean distance NMDS
nmds_jaccard <- ggplot(jac_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Jaccard Distance NMDS, Stress = ', round(jac_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_jaccard

ggsave("figures/nmds_jaccard.eps",nmds_jaccard, width = 7, height = 5, units = c("in"))

This is still a relatively high stress (>0.1) so should be interpreted with caution. Similar to Aitchison-distance nMDS but there is a little more separation of East vs West on NMDS 2 axis.

Next try a Bray-Curis NMDS, which will represent differences in presence/absence among samples and relative abundance, thus emphasizing impacts of highly abundant taxa.

bray_nmds <- metaMDS(bray_dmat, k=2, autotransform=FALSE) # Run the ordination. Distance matrix was already calculated above

Run 0 stress 0.1628608 
Run 1 stress 0.1700644 
Run 2 stress 0.1706993 
Run 3 stress 0.1574041 
... New best solution
... Procrustes: rmse 0.08465261  max resid 0.3069557 
Run 4 stress 0.1713996 
Run 5 stress 0.1805603 
Run 6 stress 0.1572102 
... New best solution
... Procrustes: rmse 0.02416992  max resid 0.1186635 
Run 7 stress 0.1498307 
... New best solution
... Procrustes: rmse 0.05787324  max resid 0.3389741 
Run 8 stress 0.1764597 
Run 9 stress 0.1512066 
Run 10 stress 0.1495162 
... New best solution
... Procrustes: rmse 0.05231193  max resid 0.3245111 
Run 11 stress 0.1652534 
Run 12 stress 0.1627925 
Run 13 stress 0.1574076 
Run 14 stress 0.1495156 
... New best solution
... Procrustes: rmse 0.0005084261  max resid 0.003155013 
... Similar to previous best
Run 15 stress 0.1498312 
... Procrustes: rmse 0.05220017  max resid 0.3259805 
Run 16 stress 0.1568971 
Run 17 stress 0.1496845 
... Procrustes: rmse 0.05079648  max resid 0.3274102 
Run 18 stress 0.2011478 
Run 19 stress 0.166024 
Run 20 stress 0.1634199 
*** Solution reached

bray_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.5 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.1495156

# Extract points from nmds and merge into data frame with metadata 
bray_nmds_df <- data.frame(bray_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
bray_nmds_df <- left_join(bray_nmds_df, metadata, by = "SampleID")
head(bray_nmds_df)

## Plotting euclidean distance NMDS
nmds_bray <- ggplot(bray_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Bray-Curtis Distance NMDS, Stress = ', round(bray_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_bray

ggsave("figures/nmds_bray.eps",nmds_bray, width = 7, height = 5, units = c("in"))

Very similar to Jaccard results. High-ish stress (0.15)

–> CONTINUE HERE

Summary

XXX